Courses
Artificial Intelligence in Games
Understand the differences between traditional AI and AI applied to game development, where other factors such as playability are more relevant that the oponent’s intelligence level. Be familiar with the practical problems when developing AI for video games, and with the several techniques applied in comercial video games. Know how to design and build an AI system for a video game independently of its genre (action, sport, strategy, narrative).
Autonomous Agents and Multi-Agent Systems
To acquire general notions about agents and multi-agent systems; knowing how to identify and classify agents and environments, according to different properties. Knowing how to develop complex systems and systems from different application areas, using an agent-oriented methodology. Knowing how to define a society of agents in order to solve a specific problem. Being able to design agents with reactive, deliberative and hybrid architectures. Being able to create societies of agents that communicate, in a practical way, using suitable languages and platforms.
Computer Graphics for Games
This course covers both theory and practice of game engine software development. It delves into the different engine subsystems including, but not limited to, rendering, character animation, and physics, and details the articulation required to support gameplay development. By the end of this course, students should understand how modern game engines work, and be able to design and develop their own game engines.
Game Design
This course grants the students the opportunity to develop their skills on experience design and prototyping for games. The learning process is sustained in the discussion of what is a game, what are its components and what is its relation to the players (having in mind their differences). It is expected that the student develop design documents and prototypes to support his/her work on the course.
Game Development Methodology
Present a vision of the different methodologies and technologies involved in the development of digital games discussing the main features and issues in each one. Grant students with conceptual tools and techniques to develop user interfaces for games with special emphasis on player controls. Develop the ability to reflect and test the player experience and gameplay. Discuss the role of conceptual modelling and user testing. Highlight the importance to take a user centred approach in the exploration of the player experience.
Multimedia Content Production
Know the different types of multimédia information and how to manipulate them to poduce multimedia content. To understand the technological constraints that affect Production. To understand critical factors affect the success of a production, namely in aspects such as capture, encoding, processing and visualization of the different media. To know the different kinds of available authoring tools. To create Multimedia contents; To identify the different contexts in which multimedia can be consumed, with emphasys on online and network issues (evaluate bandwidth, latency, synchronization, etc.) and mobile devices. Introduce some advanged multimedia usages such as procedural modelling, generative art augmented reality. Apply efficient methods of multimedia content retrieval.
Thesis
Assessing Players’ Cognitive Load in Games
Due to the exponential growth of computer technologies, video games are becoming more complex each passing year; with tasks and challenges that, very often, defy the player's cognitive abilities. Handling limitations of the Working Memory and proper Cognitive Load management is crucial when dealing with problem-solving tasks; however, these concepts appear to be highly undervalued, or even unknown, in the gaming industry. To address this problem and help game designers to better understand the intrinsic complexity of their games, this work applies the attention-shifting principles of the Time-Based Resource Sharing (TBRS) Memory Model in the game Way Out (a game we have developed from scratch). We formulated the idea of Attention-Grabbing Events and tried to incorporate them into the game, aiming to create a tool-set that estimates the player's Cognitive Load while playing a video game. To validate our hypothesis, we compared the data collected from the game with the questionnaire NASA TLX -- a subjective method that assesses the mental workload experienced during a task. Although we were unable to directly estimate the player’s Cognitive Load, we believe that this work was a step forward towards achieving that goal. The amount of Attention-Grabbing Events and gameplay time, when compared with the NASA TLX, seem to be a good indicator of Cognitive Load levels. However, the TBRS Cognitive Load formula, in its current form, does not appear to be reliable when directly applied in a general gameplay scenario -- at least following the approach we did.
Personality and Group Dynamics in Believable Multi-Agent Systems
Humans interact with computer systems and other humans in similar ways. Researchers identified personality as an important requirement for believable synthetic agents. By modeling a personality system in the agent's architecture we can bring the illusion of life to users and suspend their disbelief. Personality in synthetic agents is the main subject of this dissertation. Our focus is on group dynamics of agents interacting with users as a team with the same cooperative task. Instead of focusing on efficiency and performance we are more concerned on credibility and believability. We extended an existing model for building believable synthetic agents (SGD Model) by improving its personality system. One of the concluding remarks from previous experimental results of the original model states that very cohesive groups induce lower levels of user identification with the group. So we decided to add individualism as a new feature of the SGD Model by having a new type of incitement. The new individual motivation is permanently in conflict with the revised existing (group) motivation: help the group or do something for its own profit? Our improved personality system dictates if an agent would rather choose the former or the latter. The model was tested in a computer game. Our evaluation shows that the extended version of the SGD Model and its implementation are working as intended. Agents? behaviors and reactions follow a well defined set of rules. Additional tests are needed to verify if the extended model offers a more believable environment of cooperative synthetic agents.